Loot Table Simulator
Design weighted drop tables and verify them with Monte Carlo simulation before they ship. Measure real rarity distributions, worst-case droughts and pity behaviour — then export to Godot or Unity.
Table workspace
Edit the table on the left, verify the measured distribution on the right
Table
Total weight 1000
Pity / guarantee
Simulation
Drop entries (6)
| Item | Rarity | Weight | Min | Max | Per open | Remove |
|---|---|---|---|---|---|---|
| 50.00% | ||||||
| 30.00% | ||||||
| 12.00% | ||||||
| 6.00% | ||||||
| 1.80% | ||||||
| 0.20% |
No simulation run yet
Set an open count and press Simulate. Results appear here with expected vs observed rates and worst-case droughts.
100,000 opens · seed "jaconir"
Loot analysis
Validate the economy before you export the table
Run a simulation to analyse the economy
Expected vs observed charts stay in the workspace — the report, warnings, and EV breakdown appear here.
Designer presets
Dungeon · boss · treasure · quest · enemy · crafting · daily
Professional tips
Only what changes the table you ship
Probability
Run at least 100 ÷ rarest rate. Read expected vs observed — large deviation on rares usually means too few opens, not a broken table.
Player Psychology
Longest dry streak is the quit moment. Pity turns an unbounded worst case into a known cost players can commit to.
Economy Balance
Separate currency from gear when possible. If gold dominates chest EV, every “drop” feels like a failed roll and inflation creeps in.
Reward Frequency
Multiple rolls compound: 10% × 4 rolls ≈ 34% per open. Use Expected Value Breakdown to see what players actually earn per session.
Continue your game dev workflow
Idea → level → art → collision → audio → progression
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